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          kafka-基础知识
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        <h3 id="摘要"><a href="#摘要" class="headerlink" title="摘要"></a>摘要</h3><p>  <img src="https://kafka.apache.org/logos/kafka_logo--simple.png"></p>
<p>官网链接 <a target="_blank" rel="noopener" href="https://kafka.apache.org/documentation/#introduction">Apache Kakfa </a></p>
<p>本文主要介绍了如下内容</p>
<ol>
<li><p>Kafka 的简单介绍</p>
</li>
<li><p>Kafka中重要的基本名词概念，并给出了个人的一些分析</p>
</li>
</ol>
<p>如果疑议，欢迎gitee 提 issue，共同进步</p>
<a id="more"></a>

<h3 id="Kafka是什么"><a href="#Kafka是什么" class="headerlink" title="Kafka是什么"></a>Kafka是什么</h3><p>首先你要知道什么是<strong>消息队列</strong></p>
<p>一个分布式的消息队列技术组件，起源于领英，最开始是为了处理海量日志问题，后边逐渐进化成为业界一流的消息队列中间件。</p>
<h3 id="应用场景"><a href="#应用场景" class="headerlink" title="应用场景"></a>应用场景</h3><p>业务解耦：用于解耦不同业务系统，提供异步调用，通知的能力</p>
<p>数据处理：参与构建像Flink这样的流处理任务，可以作为实时数据源提供流式数据</p>
<h3 id="基本概念"><a href="#基本概念" class="headerlink" title="基本概念"></a>基本概念</h3><p>kafka 的几个基本概念</p>
<ul>
<li>topic：消息话题，定义了一个消息名称</li>
<li>producer：消息制造者</li>
<li>consumer：消息消费者</li>
<li>broker：接受，分发消息的机器，物理上可以理解为kafka的server</li>
<li>partition：消息分区</li>
<li>replica：消息分区的副本</li>
</ul>
<h4 id="Topic"><a href="#Topic" class="headerlink" title="Topic"></a>Topic</h4><p>消息话题？因为他有点儿类似map里的key，代表了一个消息类型，但是不是具体的消息内容<br>后边的发送消息，消费消息，都需要指定Topic<br>创建Topic命令是这样的</p>
<figure class="highlight sh"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br></pre></td><td class="code"><pre><span class="line"><span class="comment"># 你看，指定了Topic，以及kafka集群。你可可能看到 之前是ZK 的地址，这个官方已经替换掉了 https://cwiki.apache.org/confluence/display/KAFKA/KIP-377%3A+TopicCommand+to+use+AdminClient</span></span><br><span class="line">$ bin/kafka-topics.sh --create --topic quickstart-events --bootstrap-server localhost:9092</span><br></pre></td></tr></table></figure>
<h4 id="Producer（消息制造者）"><a href="#Producer（消息制造者）" class="headerlink" title="Producer（消息制造者）"></a>Producer（消息制造者）</h4><p>人如其名，负责向消息队列提交消息，启动一个producer 的时候会指定Topic，从而确定向哪个Topic发送消息。比如启动一个Producer的命令是这样的</p>
<figure class="highlight sh"><table><tr><td class="gutter"><pre><span class="line">1</span><br></pre></td><td class="code"><pre><span class="line">bin/kafka-console-producer.sh --topic quickstart-events --bootstrap-server localhost:9092</span><br></pre></td></tr></table></figure>
<p>这个命令会给你建立起一个长链接，然后你就能随便输入，每次回车都会发送一个消息到消息队列中。想结束的话 Ctrl+C</p>
<h4 id="Consumer（消费者）"><a href="#Consumer（消费者）" class="headerlink" title="Consumer（消费者）"></a>Consumer（消费者）</h4><p>同样的人如其名，负责消费Kafka消息，一般来说，消费者会解析订阅Topic，然后处理真正的消息（Message），命令如下</p>
<figure class="highlight sh"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br></pre></td><td class="code"><pre><span class="line"><span class="comment"># 注意，消费者特别指定了从头开始消费哦</span></span><br><span class="line">$ bin/kafka-console-consumer.sh --topic quickstart-events --from-beginning --bootstrap-server localhost:9092</span><br></pre></td></tr></table></figure>
<p>想结束的话 Ctrl+C</p>
<h4 id="Broker"><a href="#Broker" class="headerlink" title="Broker"></a>Broker</h4><p>负责存储具体消息数据，偏向于物理层面（非逻辑层），一个Broker可以理解为一个Kafka Server 进程或者一台运行这Kafka 服务的机器</p>
<h4 id="Partition（分区）"><a href="#Partition（分区）" class="headerlink" title="Partition（分区）"></a>Partition（分区）</h4><p>这个更倾向于是一个逻辑概念，首先一个消息可以有多个分区，每个分区会真正存储在某一个Broker上，所以最终的效果就是一个Topic对应的Message水平的分布在Broker上<br><em>为什么要水平分区？</em><br>主要是为了提高吞吐量。一般来讲（也可以从业务需求来说），业务消息生产的速率会往往远大于消费速度，也就是不会限制同一时刻前端有多少用户操作。比如没有产品会限制QPS，所有人都希望越高越好。但是后端服务可能并不能完全处理，所以Kafka 分区主要解决的是就是通过尽可能多的将消息积压起来，先完成核心业务，其他业务通过异步接收消息慢慢处理。同时引入了分区对消息进行水平分散，不仅多个Producer 能够并行像多个分区发送消息，多个Consumer也能够并行的从多个分区进行消费。另外分区也保证了负载均衡，不会让某一台机器存储过大过多的数据<br>另外通过分区机制，也可以实现资源倾斜，即量大的Topic分配更多的分区提高吞吐量，量小的分配更少的分区。</p>
<p><em>分区有没有问题 ？</em><br>有的，但是不严重。主要就是消息的全局有序性，对于同一Topic的消息来说，没办法保证消息A到达集群的时间早于消息B时，消息A就一定能够早于消息B被消费，存在两种情况</p>
<ol>
<li>消息A，B 在同一个分区上<pre><code> 1. kafka 分区都有OffSet，通过Offset kafka分区的消息是有序的（到达的顺序），所以此时消息A一定早于消息B 分发给消费者
</code></pre>
</li>
<li>消息A，B 在不同分区上<pre><code> 1. 不同分区之间，没有也不会有机制来保障分发到Consumer的顺序（会影响整体的吞吐量），所以此时没法保证消息A，B的消费顺序
</code></pre>
所以如果你需要让消息进行有序消费，那么就需要让相同规则的消息发送到同一分区，比如按uid取模。在Java API 内可以自定义producer的规则</li>
</ol>
<h4 id="Replica（副本）"><a href="#Replica（副本）" class="headerlink" title="Replica（副本）"></a>Replica（副本）</h4><p>副本=不生效的分区。副本其实全称是 【分区的副本】，它的作用就是保证消息不会丢失。上边说到了分区的概念，但是如果每个分区只有一个副本（也就是自己本身），那么风险是比较高的，对于发送到它身上的消息来说，它就是单点了，而kafka处理的场景一定是具有极大数据量，那么就需要引入副本机制，对分区进行多个备份，来保证数据不丢失<br>因为一个分区存在多个副本，那么当一个消费者确定消费某一个分区时，到底是哪个副本响应请求呢？一个还是多个还是全部？又分别有什么问题呢？</p>
<ol>
<li>一个副本响应请求<pre><code> 1. 需要解决选主问题，即以谁为准？
         1. ZK 可以解决
 2. 问题：资源浪费，其他副本并不响应消费请求    
</code></pre>
</li>
<li>多个副本响应请求<pre><code> 1. 怎么决定谁来响应请求？谁不响应请求？
         1. 无法决策，分布式目前都是解决从集群中选出一个，还不支持选出多个
         2. 如果只有一个主节点的话，主节点挂了能立刻选择，如果多个，肯定会出现数据不一致的问题，即同一时间，有的副本写入了，有的副本没写入，然后分区有序性也被破坏了
</code></pre>
</li>
<li>所有副本分别响应请求<pre><code> 1. 大家都是leader？
         1. 数据一致的问题，有的副本能读到，有的读不到
         2. 那我能不能让所有的副本都同步之后，再消费？那就回到了最开始说的，消费端一定比生产端慢，降低了生产端的吞吐量属于本末倒置
</code></pre>
</li>
</ol>
<p>所以，Kafka 实际是第一种策略，也就是一主多从。同一时刻只有一个leader 节点负责响应请求，其它副本只负责自己尽量和leader节点保持一致。后边会讲详细的细节</p>
<h3 id="总结"><a href="#总结" class="headerlink" title="总结"></a>总结</h3><p>上边基本把Kafka涉及到的角色术语都介绍了一下，但是还有很多重要的细节没有介绍，比如</p>
<ol>
<li>Kafka的消息是持久化磁盘上的，那么到底做了哪些优化来实现在廉价的磁盘上实现高速的写入？</li>
<li>Kafka 的副本机制，是怎么保证消息的不丢失的？</li>
<li>Kafka 都能提供什么样的消息交付？能否保证消息在任何情况下都只发送一次，然后只被消费一次？</li>
<li>副本的Leader 是怎么选举的？</li>
<li>分区会出现负载不均衡的情况，那么是怎么Rebalance的？</li>
<li>Comsumer是怎么确定自己消费哪个分区的？</li>
</ol>

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